Teaching & Mentoring | Raj S. Shah

Teaching experience

Courses and instruction

Teaching across cognitive science, machine learning, natural language processing, and information retrieval.

Georgia Institute of Technology

CS 7641

Human and Machine Learning

Graduate Teaching Assistant · Professor Sashank Varma

Fall 2025Fall 2023

2025

Created homework and assignments and assisted students with course capstone projects.

2023

Assisted students with course capstone projects.

CS 3790

Introduction to Cognitive Science

Graduate Teaching Assistant · Professor Sashank Varma

Spring 2024Spring 2023

Created homework and assignments for the course.

CS 6795

Introduction to Cognitive Science

Teaching Assistant

Spring 2026Fall 2025Spring 2025

CS 4650

Natural Language Processing

Graduate Teaching Assistant · Professor Alan Ritter

Fall 2022

Created coding exercises that gave students hands-on experience with natural language processing.

Birla Institute of Technology and Science, Pilani

CS F469

Information Retrieval

Undergraduate Teaching Assistant · Professor Vinti Agarwal

Fall 2020

Created a term-paper-based assignment designed to increase students’ exposure to research.

Academic Counselling Cell

Student Programs and Mentorship

Undergraduate Teaching Assistant · Professor Sangeeta Sharma

Fall 2019

Planned mentorship programs, development seminars, and community-building activities.

Mentorship philosophy

Developing independent, thoughtful researchers

My approach to mentorship centers on durable research skills, intellectual ownership, inclusion, and well-being.

I view mentorship as a collaborative process that helps people become confident, independent, and thoughtful researchers. My goal is not simply to help a mentee complete a project. I want them to develop durable skills for identifying important questions, evaluating evidence, navigating uncertainty, communicating ideas, and continuing to learn after our formal collaboration ends.

As a doctoral researcher and teaching assistant, I have mentored and collaborated with more than twenty undergraduate, master’s, and doctoral students across computer science, natural language processing, cognitive science, and human-centered AI. These experiences have taught me that effective mentorship must be individualized, structured, inclusive, and responsive to each person’s goals.

Individualized mentorship

Every mentee enters research with different preparation, interests, constraints, and aspirations. I begin by understanding what the student hopes to gain—research exposure, technical development, preparation for graduate school, or exploration of a career path—and develop an appropriate project scope and working structure.

I make the often-hidden conventions of research explicit: how to read a paper critically, conduct a literature review, formulate a tractable question, design an experiment, interpret unexpected findings, document decisions, and communicate results.

Developing research independence

I use gradual scaffolding to help mentees progress from guided participation to intellectual ownership. Early support may include structured readings, example analyses, concrete milestones, or hands-on technical work. As confidence grows, I shift toward asking questions rather than prescribing solutions.

I consider mentorship successful when a student can formulate their own questions, defend methodological choices, recognize when they need additional evidence, and make informed decisions without waiting for instructions.

Feedback, community, and well-being

Constructive feedback should be timely, specific, and directed toward the work rather than the person. I explain both what should change and why the change would strengthen the research, while adapting feedback to the project stage and the mentee’s needs.

Research inevitably includes failed experiments, rejected papers, and uncertainty. I try to normalize these experiences and create an environment where concerns can be raised early. Sustainable research requires realistic expectations, respect for personal commitments, and attention to well-being.

Inclusion and responsible research

I want students from different backgrounds to see their perspectives as substantive contributions. I encourage questions, avoid treating prior familiarity as a measure of ability, and provide multiple ways to participate in technical and conceptual discussions.

I also emphasize transparent credit, research ethics, reproducible workflows, and accessible documentation. Students should learn to ask not only whether a system works, but whose experiences are represented, how findings may be interpreted, and what consequences deployment could create.

Supporting paths beyond one project

Mentorship includes developing writing, presentation, collaboration, and career-planning skills. Depending on a student’s goals, this can involve discussing graduate programs, reviewing application materials, practicing talks or interviews, and considering opportunities across academia, industry, and public-interest research.

Students I have worked with have continued into graduate programs and research or engineering roles across universities, research institutes, national laboratories, and technology companies. These varied paths reinforce that mentorship should help people define success for themselves.

Reflection and accountability

My mentorship practice will continue to evolve. I aim to solicit feedback, reflect on which approaches are effective, and recognize when a student would benefit from guidance or resources beyond my expertise.

Ultimately, I hope mentees leave our collaboration with greater independence, stronger judgment, and the confidence to contribute their own perspectives to research communities.

Student mentorship

Research collaborators

Students I have mentored and collaborated with across research projects.

Student mentorship

Ifdita Hasan Orney

Master’s student · Stanford University

August 2024–present

Jenna Kang

PhD student · New York University

August 2024–present

Harsh Lalai

Undergraduate · BITS Pilani

July 2023–present

Incoming PhD student at Johns Hopkins University

James Jun

MS CS · Georgia Tech

January 2024–May 2025

AI/ML Research Engineer at Georgia Tech Research Institute

MS CS · Georgia Tech

January 2024–May 2024

Atith Gandhi

MS CS · Georgia Tech

January 2023–May 2024

Software Engineer at Oracle

Ananjan Nandi

MS CS · Stanford University

August 2023–May 2024

PhD student at Stanford University

Cheng Chang

MS CS · Stanford University

August 2023–May 2024

Software Engineer at Google

Siddhartha Vemuri

MS CS · Georgia Tech

January 2023–August 2023

Jason Liu

Undergraduate · University of Pennsylvania

August 2021–August 2022

Damian Rene

Undergraduate · Swarthmore College

August 2021–August 2022